Abstract
ABSTRACT Following the Fukushima Daiichi nuclear accident, fears have arisen that terrorists can cause a similar accident by acts of sabotage against nuclear facilities; as a result, the importance of nuclear security has increased. In particular, sabotage by insiders is a distinct threat to nuclear power plants. In response to limitations of physical protection system (PPS) in nuclear facilities, in this paper, we propose a behavior recognition method that is based on hand motion time-series data analysis and use deep learning to explore insider sabotage detection.
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